Optimizing LSTM-Based AI Workloads on High-Performance Computing Systems: A Scalability and Performance Evaluation

Deep learning models are increasingly used for time-series applications; however, they introduce significant computational challenges, particularly when handling the largest workload datasets. Long Short-Term Memory (LSTM) networks are widely used in time- series applications because they can learn sequential patterns and time-dependent relationships within data. Nevertheless, LSTM training is computationally expensive and time-consuming. This work investigates the performance of LSTM models across heterogeneous computing environments, focusing on the impact of sequential and parallel execution strategies. Experiments were conducted on multiple platforms with different Central Processing Unit (CPU) and Graphics Processing Unit (GPU) hardware using a 13-year (4748 observations) original dataset synthetically expanded to 47,480 observations to evaluate computational workloads rather than additional real-world observations. The results show that task-level parallel execution reduces wall-clock execution time across the tested systems, while predictive performance remains broadly comparable across the tested configurations. Furthermore, the GPU-based implementation achieved lower execution times than the CPU-based implementation in the tested configurations. Overall, this study highlights the importance of HPC and parallel computing for accelerating deep learning workflows in time series applications. The findings confirm that computational performance is highly dependent on hardware capabilities and execution strategies, although accuracy remains stable across heterogeneous platforms.